Source area identification with observation from limited monitor sites for air pollution episodes in industrial parks

Huang, Zihan; Wang, Yuan; Yu, Qi; Ma, Wei; Zhang, Yan

doi:10.1016/j.atmosenv.2015.08.048

Cited by 25 publications

(13 citation statements)

References 16 publications

(31 reference statements)

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“…Single pollution episode would be captured once there was one monitor covered by the simulated envelope. The collection of potential source locations for source identification was obtained based on the result back-calculated by the source area analysis method (described in [32]). Source area analysis method is an insightful way to reconstruct source terms with limited or sufficiently many monitors.…”

Section: Data Configurationmentioning

confidence: 99%

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Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

Huang

Liu

et al. 2019

Atmosphere

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Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H 2 S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (r d ) and source identification efficiency (r b ). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of r d and r b were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks.

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Section: Data Configurationmentioning

confidence: 99%

“…The source area analysis method established in our previous paper [32] was employed to perform the source identification. It provides an approach to obtain the source area by means of meteorological data and concentration measurements.…”

Section: Source Area Analysismentioning

confidence: 99%

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

Huang

Liu

et al. 2019

Atmosphere

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“…This results in a linear problem for which we can still write an explicit analytical solution. We first rewrite the likelihood distribution appearing in (18) as…”

Section: Unconstrained Emission Ratesmentioning

confidence: 99%

“…Although some attempts have been made to apply Bayesian methods in the context of atmospheric dispersion problems, their application is much less extensive than other approaches. Some examples include Huang et al [18] who employ a Bayesian method for identifying the location and emission rate for a single point source by incorporating a Gaussian puff solution, while Keats et al [22] use Bayesian inference to identify emissions in a more complex urban environment. In both cases, the authors use an adjoint approach to efficiently solve the advection-diffusion PDE and evaluate the likelihood function using a Markov Chain Monte Carlo algorithm.…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimation of airborne fugitive emissions using a Gaussian plume model

Hosseini

2016

Atmospheric Environment

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A new method is proposed for estimating the rate of fugitive emissions of particulate matter from multiple time-dependent sources via measurements of deposition and concentration. We cast this source inversion problem within the Bayesian framework, and use a forward model based on a Gaussian plume solution. We present three alternate models for constructing the prior distribution on the emission rates as functions of time. Next, we present an industrial case study in which our framework is applied to estimate the rate of fugitive emissions of lead particulates from a smelter in Trail, British Columbia, Canada. The Bayesian framework not only provides an approximate solution to the inverse problem, but also quantifies the uncertainty in the solution. Using this information we perform an uncertainty propagation study in order to assess the impact of the estimated sources on the area surrounding the industrial site.

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“…Taking the positioning of emission sources as an example, the obtained solution domain was infinite if no prior information was available, or only measurements from 1-2 stations were given. The results obtained from source area analysis method shows that a fan-shaped source area in the upper wind of the monitors could be determined based on measurements from one monitor [7], and the source area became narrower when measurements from two monitors were given. Significantly, source areas in the latter case were still not small enough to precisely detect the real source.…”

Section: Introductionmentioning

confidence: 99%

Identifying Key Potential Source Areas for Ambient Methyl Mercaptan Pollution Based on Long-Term Environmental Monitoring Data in an Industrial Park

Liu

Huang

et al. 2018

Atmosphere

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Precise source identification for ambient pollution incidents in industrial parks were often difficult due to limited measurements. Source area analysis method was one of the applicable source identification methods, which could provide potential source areas under these circumstances. However, a source area usually covered several sources and the method was unable to identify the real one. This article introduces a case study on the statistical source identification of methyl mercaptan based on the long-term measurements, in 2014, in an industrial park. A procedure for statistical source area analysis was established, which contains independent pollution episode extraction, source area calculation scenario definition, meteorological data selection, and source area statistical analysis. A total of 414 violation records were detected by five monitors inside the park. Three kinds of calculation scenarios were found and, finally, three key source areas were revealed. The typical scenarios of source area calculations were described in detail. The characteristics of the statistical source areas for all pollution episodes were examined. Finally, the applicability of the method, as well as the source of uncertainties, was discussed. This study shows that more concentrated source areas can be identified through the statistical source area method if several excessive emission sources exist in an industrial park.

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Source area identification with observation from limited monitor sites for air pollution episodes in industrial parks

Cited by 25 publications

References 16 publications

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

Bayesian estimation of airborne fugitive emissions using a Gaussian plume model

Identifying Key Potential Source Areas for Ambient Methyl Mercaptan Pollution Based on Long-Term Environmental Monitoring Data in an Industrial Park

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